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AlphaTrend managed-futures trend: complete trial lineage and evidence boundary

By , Canli Capital

Short title: AlphaTrend: managed-futures trend lineage
Author: Arhan Canli, Founder, System Architect, and Quantitative Researcher, Canli Capital
Research system: ALPHAC / AlphaForge
Family key: managed_futures_trend
Status: public research record; not peer reviewed; not an investment solicitation
Evidence date: 2026-08-22

Abstract

AlphaTrend is ALPHAC's managed-futures trend program. It takes volatility-normalized directional positions from medium- and long-horizon price trends across liquid exchange-traded market proxies, then combines them under a covariance-aware risk budget. This paper reconciles all 21 charged hypothesis identities in the family rather than presenting the strongest backtest alone.

The result is promising as a diversifier but statistically unresolved. Nineteen identities have finite Sharpe estimates ranging from -0.238 to +0.345. Six identities retain complete walk-forward artifacts, seven retain source-bound summary reports, and eight retain immutable ledger summaries without full curves. Thirteen have persisted artifact-era DSR values; zero clear the 0.95 gate. The 17-market baseline had Sharpe 0.289 and 9.69% vol-matched maximum drawdown in a replica screen. Correlation whitening reduced Sharpe to 0.186, a single 110-session signal reached 0.271, and expanding to 33 ETFs reduced Sharpe to 0.128 while worsening vol-matched drawdown to 14.42%. AlphaTrend remains a paper evidence-collection sleeve, not an established forward result.

Claim boundary

This paper establishes the implemented hypothesis, complete family trial count, persisted results, evidence gaps, and historical decisions. It does not establish future return, an achieved forward Sharpe, expected maximum drawdown, capacity, live-money performance, or external attestation. Paper execution through Alpaca is clearly separated from historical simulation.

Economic hypothesis and literature

The falsifiable hypothesis is that the sign and magnitude of an asset's medium-horizon excess-price move contains information about its next return, and that combining trends across genuinely different markets improves risk-adjusted performance. Possible mechanisms include slow information diffusion, investor underreaction, positioning constraints, and compensation for bearing reversal risk. These mechanisms motivate a prior; they do not validate ALPHAC's implementation.

ALPHAC implementation

The core signal blends three volatility-normalized price trends:

trend(i,t,L) = [log P(i,t) - log P(i,t-L)] / [sigma(i,t) * sqrt(L)]
signal(i,t)  = mean(trend(i,t,63), trend(i,t,126), trend(i,t,252))

Weights are determined from information available through session t and become effective on the next session. The principal ETF implementation uses 17 proxies spanning equities, rates, commodities, and currencies, a 33-session volatility estimate, covariance-aware allocation, monthly rebalancing, gross and name caps, 6 basis points per side, and 50 basis points annualized short borrow. The full walk-forward path uses purged anchored-expanding windows. A separate futures-proxy experiment is retained as a distinct identity because its instruments and data path are different.

Trial lineage

Identity group Charged identities Evidence retained
Core signal/cadence/universe studies 14 Six full walk-forwards; eight immutable ledger summaries
Allocator and signal-horizon screen 3 Source-bound summary report
Fixed and selected breadth screen 4 Source-bound summary report
Total 21 Every identity remains in the family and union burden

The breadth count includes two post-hoc procedures: dropping the largest full-sample PnL contributor and greedily adding markets only when estimated effective breadth rises. These are not free diagnostics. They generated measured return configurations and are charged as selection identities. Rounded equality between the ARP baseline and breadth baseline is not used to collapse them because their exact return curves were not preserved together.

The machine-readable source of truth is /glassbox/alphatrend_family.json. It binds every hypothesis key to the immutable ledger, strongest available artifact, source hash, exact configuration, evidence grade, and missing measurements.

Complete walk-forward evidence

Configuration Sharpe Max DD Turnover/yr Artifact-era DSR Decision
63/126/252 blend, 17 ETFs, 21-session rebalance 0.263 10.29% 6.44× ~0.000 Fail DSR
126-session signal 0.175 13.32% 5.47× 0.704 Fail DSR
252-session signal 0.182 11.52% 4.93× 0.700 Fail DSR
Blend, 5-session rebalance 0.278 10.91% 12.31× 0.798 Fail DSR
Blend, 10-session rebalance 0.311 10.19% 9.39× 0.816 Fail DSR
Real-futures proxy path -0.238 16.03% 8.97× ~0.000 Fail DSR

The highest complete-artifact Sharpe is not adopted as proof. It was one of several cadence and signal choices, remained below the historical DSR gate, and increased turnover materially.

Allocator and signal-horizon screen

All three rows share 5,161 sessions and are historical summaries without daily curves or kurtosis. Maximum drawdown is shown after matching each stream to baseline realized volatility.

Variant Sharpe Realized vol Vol-matched max DD Turnover/yr Artifact-era DSR
Baseline 63/126/252 blend 0.289 4.49% 9.69% 4.65× 0.000
Correlation-whitened allocator 0.186 2.75% 12.76% 7.79× 0.000
Single 110-session signal 0.271 4.56% 10.57% 5.31× 0.000

Correlation whitening did not improve the baseline. The 110-session signal was close but weaker. Neither is promoted.

Breadth experiment

Basket/procedure Sharpe N_eff Avg. pair correlation Vol-matched max DD Decision
Baseline 17 0.289 13.52 0.127 9.69% Control
Expanded 33 0.128 11.01 0.250 14.42% Reject
Expanded minus largest contributor (SHY) 0.217 10.11 0.264 10.04% Reject
Greedy N_eff-pruned 22 0.204 14.76 0.153 9.58% Reject

More tickers did not mean more independent bets. The 33-market expansion raised average correlation, reduced effective breadth, reduced Sharpe, and worsened drawdown. The selected 22-market basket raised estimated N_eff relative to baseline but still reduced Sharpe. This is a useful null: breadth acquisition must be exposure-aware and independently validated, not counted by ticker quantity.

Uncertainty, missing evidence, and capacity

Eight identities have only immutable ledger summaries; two of those have non-finite Sharpe because their reduced instrument sets did not support a valid estimate. They remain charged and visible. Summary-only ARP and breadth reports preserve no exact daily curves or kurtosis, so current-union DSR cannot be honestly reconstructed for those rows. The family packet therefore reports original DSR only where it exists and never substitutes a rounded estimate.

No persisted AlphaTrend capacity sweep measures market impact, ETF creation/redemption effects, borrow availability, or stressed liquidity across AUM. Capacity is unmeasured. A gross cap and liquid ticker list are implementation controls, not capacity evidence.

Paper execution and forward burden

AlphaTrend is one of the broker-executed Alpaca paper sleeves. Its public paper record is useful only if it remains continuous, broker-reconciled, and configuration-controlled. The record is too short to establish a forward Sharpe or the program's expected maximum-drawdown objective. Historical simulations selected from the 228-identity union cannot be relabelled as forward evidence.

The family earns at most one sleeve slot. It remains in evidence collection because the mechanism is distinct and the paper program needs diversifiers, not because a backtest passed the governing standard. Any production change requires a versioned declaration; any future research variant must be registered before returns are inspected.

Reproduction and provenance

Run from the repository environment:

uv run python scripts/audit_alphatrend_family.py
uv run python scripts/build_trial_packet_manifest.py
uv run pytest -q tests/unit/test_alphatrend_family.py tests/unit/test_trial_packet_manifest.py

The audit reads only immutable experiment ledgers and persisted artifacts. It opens no holdout, records no trial, and fails if identity count, artifact Sharpe, source hash, or evidence mapping drifts. Authorship and research leadership are credited to Arhan Canli; that credit does not alter the statistical claim boundary.